Arrival Patterns Research Articles

Short-term inbound passenger flow prediction at high-speed railway stations considering the departure passenger arrival pattern

Accurate prediction of short-term inbound passenger flow at high-speed railway stations is of great significance for the refined operation of stations, the formulation of emergency plans, and the provision of intelligent services. The arrival of passengers traveling on the same train at the same station shows a similar pattern, which is called the departure passenger arrival pattern (DPAP). The short-term inbound passenger flow at the station is composed of the short-term inbound passenger flow of all waiting trains within the same period. Inspired by this, this paper develops an ensemble prediction model based on the time series decomposition modeling strategy to introduce the DPAP to the short-term inbound passenger flow prediction at stations. Firstly, we propose a new framework for studying the DPAP to calculate the fitted station short-term inbound passenger flow, which is only affected by the DPAP. During this process, we find that 7minutes is the optimal time granularity. Secondly, based on the singular spectrum analysis, we prove that the DPAP is the determining factor affecting the station short-term inbound passenger flow. Finally, we propose an ensemble prediction model that considers the DPAP to achieve short-term inbound passenger flow prediction at stations. The model consists of two parts: the deterministic and stochastic components prediction, where the former is predicted by the fitted station short-term inbound passenger flow, and the latter is achieved by the combination of historical stochastic components and weather type with the help of the Seq2Seq model based on time attention mechanism. Using real inbound passenger flow data, we compare the proposed model with 13 benchmark models and the results show that under different training and prediction steps, our model achieves optimal prediction performance, whether in all-day period and the busiest period of the station. Through further ablation experiments, it has been proven that the introduction of the DPAP effectively improves the prediction accuracy. Our model can provide scientific support for the intelligent operation of stations and the refined management of passenger flow.

Request Deadline Split and Interference‐Aware Request Migration in Edge Cloud

ABSTRACTEdge computing extends computing resources from the data center to the edge of the network to better handle latency‐sensitive tasks. However, with the rise of the Internet of Things, edge devices with limited processing capabilities face difficulties in executing requests with fluctuating request peaks. In order to meet the deadline constraints of latency‐sensitive tasks, a feasible solution is to offload some latency‐sensitive tasks to other nearby edge devices. This article studies the problem of request migration in edge computing systems and minimizes the request deadline violation rate based on actual online arrival patterns, performance interference phenomena, and deadline constraints. Since a request contains multiple services and request migration will lead to changes in server resource competition pressure, we split the problem into three sub‐problems, dividing the request deadline to determine the maximum response time of the service, determining the performance of the service under different resource pressures and the request migration strategies. To this end, we propose two deadline splitting methods, a performance interference model under multi‐resource pressure, and two heuristic request migration strategies. Since this article considers online edge scenarios, the number and type of requests are black boxes. We conduct simulation experiments and find that our method has only one‐third the number of request violations of other methods.

A dynamic yard space reservation algorithm based on reward-penalty mechanism

Increasing throughput at container terminals leads to higher yard occupancy rates, reduced yard service capacity, and inefficient use of space. These issues result in longer waiting times for trucks, underutilized yard slots, and longer distances between berth positions and the yard, particularly under high occupancy conditions. Traditional methods for allocating yard space fail to adequately consider dynamic container arrival patterns, conflict between container operations, and efficient use of yard resources. This paper introduces an innovative approach to improve yard efficiency by dividing yard container retrieval into time intervals and dynamically reserving yard slots within each interval. This method aims to minimize operational conflicts, reduce distances between blocks and berths, and avoid excessive reservations. We propose a dynamic reservation algorithm based on a reward-penalty mechanism to handle the problem's complexity and real-time demands. Furthermore, a time-series reward-penalty mechanism, which considers past reservation feedback and future reservation potential, ensures the concentration and continuity of container groups in the yard. Our evaluation metrics and multiple experimental scenarios demonstrate that this method significantly enhances yard efficiency and overall port productivity compared to static and other dynamic algorithms.

Population Movement and Poliovirus Spread across Pakistan and Afghanistan in 2023.

Population movement dynamics are a critical part of understanding communicable disease transmission patterns and determining where, when, and with whom to deliver appropriate prevention interventions. This study aimed to identify the origin of the Afghan population and their patterns of movement within Karachi, to assess the polio vaccination status of children under the age of five, and to investigate the travel history and guest arrival patterns of individuals from Afghanistan and other regions known to be affected by wild poliovirus type 1 (WPV1) within the past six months. A cross-sectional survey was conducted in selected 12 union councils of Karachi, Pakistan. The data were collected through interviews with Afghan household members and from the frontline workers (FLWs) responsible for the polio vaccination of the children of the same households. Cohen's kappa was used to check the agreement between information provided by the household participant and FLWs. A total of 409 Afghan household members were interviewed. Travel of any household member outside the city within the last six months was reported by 105 (25.7%) participants, 140 (34.2%) hosted guests within the last six months, and 92 (22.5%) participants reported that guest children were vaccinated in their households. A total of 230 (56.2%) participants observed polio teams at relatives' households within Karachi, and 127 (31.1%) observed polio teams at relatives' households outside Karachi in different districts of Pakistan and Afghanistan. Fair to moderate agreement was observed between information provided by the household members and FLWs on the variable's duration of living at current residence (Kappa = 0.370), travel history (Kappa = 0.429), guest arrival (Kappa = 0.395), and household children vaccinated for OPV (Kappa = 0.419). Substantial population mobility was observed between Afghanistan and Pakistan as well as significant movement of the Afghan population within Karachi in the last six months. These findings warrant attention and targeted implementation of interventions to enhance and sustain both routine and supplementary immunization activities within this demographic group.

Mapping evidence on the factors contributing to long waiting times and interventions to reduce waiting times within primary health care facilities in South Africa: A scoping review.

Globally, reduction of patient waiting time has been identified as one of the major characteristics of a functional health system. In South Africa, 83% of the general population visiting primary healthcare (PHC) facilities must contend with long waiting times, overcrowding, staff shortages, poor quality of care, an ineffective appointment booking system, and a lack of medication. These experiences may, in turn, affect how patients view service quality. This scoping review was guided by Arksey and O'Malley methodological framework. The primary literature search of peer-reviewed and review articles was achieved through PubMed/MEDLINE, Google Scholar, Science Direct, and World Health Organization (WHO) library databases, using waiting times, outpatient departments, factors, interventions, and primary healthcare facilities as keywords. Two independent reviewers screened abstracts and full articles, using the set inclusion and exclusion criteria. We used NVIVO® version 10 software to facilitate thematic analysis of the results from included studies. From the initial 250 records screened, nine studies were eligible for inclusion in this scoping review. Seven papers identified the factors contributing to waiting time, and five papers mentioned effective interventions implemented to reduce waiting times within PHC facilities. Our analysis produced three (patient factors, staff factors, and administrative systems) and two (manual-based waiting time reduction systems and electronic-based waiting time reduction systems) main themes pertaining to factors contributing to long waiting times and interventions to reduce waiting times, respectively. Our results revealed that the patients, staff, and administrative systems all contribute to long waiting times within the PHC facilities. Patient waiting times recorded a wider and more evenly spread patient arrival pattern after the identified interventions in our study were implemented. There is a need to constantly strategize on measures such as implementing the use of an electronic appointment scheduling system and database, improving staff training on efficient patient flow management, and regularly assessing and optimizing administrative processes. By continuously monitoring and adapting these strategies, PHC facility managers can create a more efficient and patient-centered healthcare experience.

Flexible yard crane scheduling for mixed railway and road container operations in sea-rail intermodal ports with the sharing storage yard

The sea-rail intermodal port, serving as the “cornerstone” supporting the seamless connection of landside and quayside transportation, plays a crucial role in determining the service level of the container multimodal transport through port’s operational organization efficiency. With on-dock rails, the physical integration between railways and water transport is smoothly achieved. However, within the port, the diversity in container flows adds complexity to operational organization and decision-making. In this paper, we formulate a flexible crane scheduling model to reduce the mutual interference during the mixed operations of railway containers and road containers in the sharing yard. The model addresses the time window constraints of railway containers required by train schedule, and the uncertain scenarios of external truck arrivals. To solve this problem, we design a two-stage approach that encompasses a task reassignment phase and a sequential decision-making phase. Furthermore, we test the model and algorithm performance through various experiments of different scales. Our results show that the designed algorithm designed solves instances of 20 tasks in 18.7 s, compared to 3600 s for the model solver. It efficiently handles real-world instances with over 200 tasks and 100 scenarios. Finally, sensitivity analysis is carried out to capture the influence of railway container ratio, truck arrival patterns, and train time windows on yard operations. Under intensive operation, we find that road containers, following a uniform arrival pattern, are least affected by railway container operations when the proportion of railway containers in the sharing block ranges from 70% to 80%. We also observe that the flexibility of crane service increases as the time window for railway container operations becomes more lenient.

One-stage estimation of cyclic arrival rates using license plate recognition data

Traffic arrival on urban roads usually reveals time-varying rates due to the effects of signal control, motivating us to investigate the distribution of arrival rates within the signal cycle. However, such information is not readily available as only a portion of arrival traffic can be observed in the field. Previous studies have utilized different data sources and proposed probability-based models to solve the estimation problem. However, these studies rely heavily on the assumption that the observed vehicles are evenly distributed in the arrival traffic flow and exact signal timings are known. This study uses license plate recognition (LPR) data collected at adjacent signalized intersections to estimate cyclic arrival rates in a historical period. A probability-based model is formulated by parameterizing the vehicle arrival distribution as a mixture of circular distributions. This facilitates us to describe similar traffic arrival patterns over different signal cycles and capture multiple arrival platoons from different signal phases within the signal cycle. The proposed model is validated in a field experiment with different arrival patterns and traffic conditions. Model performance under both full and partial monitoring of LPR sensors at the upstream intersection is analyzed. The robustness of the proposed model with unevenly sampled observations in arrival traffic flow is also investigated.

Two-types of service facilities in interconnected stochastic queueing and queueing-inventory system with marked Markovian arrival process

This article explores the performance of interconnected queueing systems and queueing-inventory systems (IQSQIS) in stochastic modeling. IQSQIS features two types of service facilities with dual service stations: one providing non-commodity service with a multi-server, and another handling commodity sales with a single server. As directed, a customer type i,i=1,2 can approach the station i,i=1,2 whose arrival pattern follows the marked Markovian arrival process (MMAP) since we assume that both service stations have an equal arrival phase. The IQSQIS gives an offer to the type-2 customer to choose type-1 service at the end of their service completion. Numerical results suggest that this option reduces customer wait times, orbit size, and overall system costs. The comparative analysis of heterogeneous and homogeneous servers in station-1 and queue-dependent and non-queue-dependent service facilities in station-2 is presented and investigated using the numerical outputs.

Mapping Web Service Characteristics to Queueing Theory Models for Performance Analysis

The article presents a comprehensive analysis of mapping the characteristics of Web services to queueing theory models, specifically the M/M/1 queueing model. The authors aim to establish a theoretical foundation for modeling the performance of Web services and deriving response time formulae. The key points covered in the article are as follows: 1. Analyzing the characteristics of Web services: The article examines the six fundamental characteristics of queueing systems, including arrival patterns, service patterns, queue discipline, system capacity, and the number of servers, and maps them to the Web service environment. 2. Representation using Kendall's notation: The Web service characteristics are represented using Kendall's notation, resulting in the M/M/1/∞/FCFS model, where M denotes exponential distributions for arrival and service patterns, 1 represents a single server, ∞ represents infinite system capacity, and FCFS represents the first-come, first-served queue discipline. 3. Derivation of response time formulae: The article provides a detailed derivation of the response time formulae for the M/M/1 model using stochastic processes, Markov chains, and the birth-death process. The derivation incorporates steady-state conditions and results in a formula that calculates the probability of completing a request within a user-specified response time. 4. Practical applications: The derived response time formula can be used by service providers to determine the probability of completing user requests within specified Service Level Agreements (SLAs). By setting a threshold value (filter value), the service provider can select requests with a higher probability of completion for further processing. Overall, the article contributes to the understanding of Web service performance modeling by establishing a theoretical foundation based on queueing theory. The mapping of Web service characteristics to the M/M/1 model and the derivation of response time formulae provide valuable insights for service providers to analyze and optimize the performance of their Web services while adhering to SLAs

An online dynamic dual bin packing with lookahead approach for server-to-cell assignment in computer server industry

Efficient production scheduling in computer server industry remains a critical challenge. This challenge becomes particularly acute during the testing phase, where servers are assigned for testing in a modular environment consisting of test banks. Unique characteristics such as online arrival of servers known only over a short time window, skewed arrival pattern, power and cooling compatibility constraints, and dynamic assignment complicate decision making, a situation we observed at our industry partner’s site, which often leads to missing due dates and loss of customer trust. Motivated by this challenge, we introduce an Online Dynamic Dual Bin Packing with Lookahead problem and propose an integer linear programming model that maximizes the number of assigned servers. To efficiently solve this model, we decompose the problem into a set of subproblems for a given lookahead length. A ‘2-phase’ computational framework is proposed that seamlessly integrates mathematical programming with genetic algorithm. Based on realistic data available from a server manufacturer, our findings suggest that solutions are sensitive to (i) the length of the lookahead window, (ii) testing capacity and server arrival pattern, (iii) test processing time requirement, and (iv) physical allocation of servers.

Shared e-scooters: A last-minute mode?

In this paper we assess whether shared e-scooters are being used in hurried situations to avoid arriving late, making trips we define as last-minute trips. We identify trip characteristics for last-minute trips using data from shared e-scooters in Stockholm. Trip arrival time patterns, distances, durations, and speeds are analyzed in relation to desired arrival times. We observe a peak in the number of trips ending before the full hour and a smaller peak of trips ending before the half hour of weekday mornings. These trips have lower average trip durations and higher average speeds, indicating a larger share of last-minute trips. Arrival patterns shift when the assumed preferred arrival time changes, and when the risk associated with arriving late increases. Furthermore, there is an increased share of trips that started close to public transportation around the full hour, indicating that a portion of last-minute trips may also be last-mile trips. Our results show that while last-minute trips are infrequent per user, they do make up a relatively large share of weekday morning trips. These results highlight an important market segment for shared e-scooter services and may also explain why some research reports e-scooter usage replacing walking.

A hybrid simulation approach to predicting noise in restaurants

High levels of noise are a well-known source of occupant discomfort in dining environments, and noise ranks highly among the most common customer complaints in restaurant reviews. Prior work has focused on measuring existing restaurant soundscapes and attempting to predict acoustic performance; however, current techniques are still limited in accuracy for restaurants with time-varying occupancy and complex layouts. This presentation demonstrates a novel approach to predicting ambient noise in restaurants via a hybrid simulation framework. Acoustical computations for a given restaurant space are performed in a room acoustics model, while a discrete event simulation (DES) model is used to simulate the arrival patterns of patrons and their conversational behaviors. Predictions from the room acoustics model inform the DES model, allowing for noise level predictions at each seat position. Results from the prediction model are validated against measurements in a case study restaurant. Further development of this framework may support the practical assessment of design interventions in any restaurant while also accounting for time-varying patterns of occupancy.

Agile C-states: A Core C-state Architecture for Latency Critical Applications Optimizing both Transition and Cold-Start Latency

Latency critical applications running in modern datacenters exhibit irregular request arrival patterns and are implemented using multiple services with strict latency requirements (30 μs –250 μs ). These characteristics render existing energy saving idle CPU sleep states ineffective due to the performance overhead caused by the state’s transition latency. Besides the state transition latency, another important contributor to the performance overhead of sleep states is the cold-start latency, or in other words, the time required to warm-up microarchitectural state (e.g., cache contents, branch predictor metadata) that is flushed or discarded when transitioning to a lower-power state. Both the transition latency and cold-start latency can be particularly detrimental to the performance of latency critical applications with short execution times. While prior work focuses on mitigating the effects of transition and cold-start latency by optimizing request scheduling, in this work, we propose a redesign of the Core C-state architecture for latency-critical applications. In particular, we introduce C6Awarm a new Agile Core C-state that drastically reduces the performance overhead caused by idle sleep state transition latency and cold-start latency, while maintaining significant energy savings. C6Awarm achieves its goals by implementing 1) medium-grained power gating, 2) preserving the microarchitectural state of the core and 3) by keeping the clock generator and PLL active and locked. Our analysis for a set of microservices based on an Intel Skylake server, shows that C6Awarm manages to reduce the energy consumption by up to \(70\% \) with limited performance degradation (at-most \(2\% \) ).

Exploring non-Markovian dynamics: Stochastic analysis of a single-server retrial queue with recurrent clients and balking behavior under extended Bernoulli vacations

The present study delves into the dynamics of a specific form of queueing system described as an M/G/1 retrial queue. Here, the queue comprises two distinct categories of clients: transit clients and recurrent clients. Transit clients are those who appear at the queue following a Poisson process, reflecting a random arrival pattern commonly seen in queueing scenarios. On the other hand, recurrent clients are predefined entities who immediately rejoin the queue once they've been served, demonstrating repetitive behavior in their interactions with the system. Once the server completes servicing a client, it initiates a vacation period. Moreover, in this approach, an optional extended vacation is also taken into account, i.e., the server may opt to indulge in an extended vacation following the initial essential Bernoulli vacation. Also, the consumers are allowed to balk. Further, the ergodicity requirement for the system's stability and then analytical findings for the stationary distribution are obtained. Additionally, various performance metrics for the system are also established. Furthermore, a comprehensive cost function is formulated and further optimized by incorporating a particle swarm optimization (PSO) approach. The convergence analysis is conducted as well, which is supported by illustrative figures. As a result, this work provides a beneficial understanding of enhancing the efficiency of such intricate queueing systems.

Low and Delayed Patient Arrival Rates during COVID-19 Pandemic in Acute Coronary Syndrome Patients: A Study from Tertiary Cardiac Center.

The footfall and admission rates post-COVID-19 scenario have come down in the case of patients with cardiac problems with larger delays leading to complications as per several studies. Studies have primarily focused on the disruptions due to lockdown but not much study has been done to understand how it has affected the lifestyle of the patients and changed the mentality leading to lower patient arrivals. The current study deals with understanding how the patient arrival pattern has changed and what are the factors affecting the same during COVID-19 times. Analysis of the data for patients with acute coronary syndrome from April to June for pre (2019)- and post (2020)-COVID-19 times admitted to a government cardiology and cardiac care hospital is studied for patient-based and accessibility-based parameters. A significant reduction in admissions (4230-880) with higher arrival times from the onset of symptom (80% rise) although the overall mean distance traveled reduced (63.8-47.4 km) leading to greater health risks. Furthermore, problems due to physical inactivity, diabetes, smoking, and drinking have also risen by about 3%-5% in each case. The need for better health-care system connectivity and the need for online platforms-based consultation systems, especially in times of such a pandemic have been highlighted. The results from this study will be helpful in addressing the issues related to delayed care for heart patients, thereby helping in reducing the mortality rate and improving overall health.

Matching Tasks and Workers under Known Arrival Distributions: Online Task Assignment with Two-sided Arrivals

Efficient allocation of tasks to workers is a central problem in crowdsourcing. In this article, we consider a setting inspired by spatial crowdsourcing platforms, where both workers and tasks arrive at different times, and each worker-task assignment yields a given reward. The key challenge is to address the uncertainty in the stochastic arrivals from both workers and the tasks. In this work, we consider a ubiquitous scenario where the arrival patterns of worker “types” and task “types” are not erratic but can be predicted from historical data. Specifically, we consider a finite time horizon T and assume that in each time-step the arrival of a worker and a task can be seen as an independent sample from two (different) distributions. Our model, called Online Task Assignment with Two-sided Arrival (OTA-TSA), is a significant generalization of the classical online task assignment problem when all the tasks are statically available. For the general case of OTA-TSA, we present an optimal non-adaptive algorithm (NADAP), which achieves a competitive ratio (CR) of at least 0.295. For a special case of OTA-TSA when the reward depends only on the worker type, we present two adaptive algorithms, which achieve CRs of at least 0.343 and 0.355, respectively. On the hardness side, we show that (1) no non-adaptive can achieve a CR larger than that of NADAP, establishing the optimality of NADAP among all non-adaptive algorithms; and (2) no (adaptive) algorithm can achieve a CR better than 0.581 (unconditionally) or 0.423 (conditionally on the benchmark linear program), respectively. All aforementioned negative results apply to even unweighted OTA-TSA when every assignment yields a uniform reward. At the heart of our analysis is a new technical tool, called two-stage birth-death process , which is a refined notion of the classical birth-death process. We believe it may be of independent interest. Finally, we perform extensive numerical experiments on a real-world rideshare dataset collected in Chicago and a synthetic dataset, and results demonstrate the effectiveness of our proposed algorithms in practice.

Evaluation of the behavior of prices and market arrivals of tomatoes – A case study of West Bengal, India

The oscillations in prices and arrivals of agricultural goods are primarily influenced by the seasonal character of the agricultural production system. Here, we investigate the trends and seasonal price dynamics concerning market arrivals of tomatoes in West Bengal, India. Monthly data on arrivals and prices of tomatoes were collected between 2013-14 and 2019-20. Both the ordinary least square and the twelve-month moving average techniques were employed to identify the long-term trends and seasonal variations in price and arrival patterns. The market arrivals exhibit negative trends in three markets, except for Barasat and Bardhaman, where the observed values are not statistically significant. However, the lone market in Diamond Harbour shows a positive and significant trend in arrivals. Regarding current prices, all markets, except for Chakdah and Siliguri, exhibit positive and significant trend. Seasonality is more prominent in market arrivals than the price for all the markets. Results suggest the variability in arrivals is proportional to the number of arrivals in the market, in turn, larger arrivals lead to higher variability. Price variations are substantial and exhibit relative stability both within and across markets, unlike arrivals which show more fluctuation. Price fluctuations are significant not only within a given year but also between different years. Additionally, an inverse relationship between current prices and market arrivals suggest as arrivals increase, prices tend to decrease and vice versa. The study provides new insights into the trends in market arrivals and seasonal price dynamics of tomatoes in West Bengal, eastern India.

Exploring the Influencing Factors and Arrival Patterns of Inbound Tourism in Nepal

ABSTRACT Understanding the factors influencing a country's inbound tourism is crucial for shaping effective tourism strategies. This study analyzed historical data from 1964 to 2021 on tourist arrivals, duration of stay, GDP per capita, migration, heritage and attractions, COVID-19 impacts, population growth, and bilateral relationships in Nepal. Initially, regression analysis identified the statistical significance (p-value < 0.05) of these factors on Nepal's inbound tourism. Subsequently, the significant factors were modeled and visualized using neural networks. Historical arrival patterns were then identified using self-organizing maps. The results indicated that population growth, GDP, length of stay, and bilateral relationships positively influenced international arrivals, while migration, heritage, and COVID-19 had negative effects. Five distinct historical arrival patterns were identified form self organizing map comparing with the key drivers of inbound tourism. These findings are vital for tourism planners and policymakers, providing essential insights for the strategic planning and sustainable management of tourism in Nepal.

Spatial variation in spring arrival patterns of Afro‐Palaearctic bird migration across Europe

AbstractAimGeographical patterns of migrant species arrival have been little studied, despite their relevance to global change responses. Here, we quantify continent‐wide interspecific variation in spatiotemporal patterns of spring arrival of 30 common migrant bird species and relate these to species characteristics and environmental conditions.LocationEurope.Time Period2010–2019.Major Taxa StudiedBirds, 30 species.MethodsUsing citizen science data from EuroBirdPortal, we modelled arrival phenology for 30 Afro‐Palaearctic migrant species across Europe to extract start and duration of species arrival at a 400 km square resolution. We related inter and intraspecific variation in arrival and duration to species characteristics and temperature at the start of the growing season (green‐up).ResultsSpatial variation in start of arrival times indicates that it took, on average, 1.6 days for the leading migratory front to move northwards by 100 km (range: 0.6–2.5 days). There was a major gradient in arrival phenology, from species which arrived earlier, least synchronously, in colder temperatures and progressed slowly northwards to species which arrived later, most synchronously and in warmer temperatures and advanced quickly through Europe. The slow progress of early arrivers suggests that temperature limits their northward advance; this group included Aerial Insectivores and species wintering north of the Sahel. For the late arrivers, which included species wintering further south, seasonal resource availability in Africa may delay their arrival into Europe.Main ConclusionsWe found support for the green‐wave hypothesis applying widely to migratory landbirds. Species arrival phenologies are linked to ecological differences between taxa, such as diet, and wintering location. Understanding these differences informs predictions of species' sensitivity to global change. Publishing these arrival phenologies will facilitate further research and have additional conservation benefits such as informing designation of hunting seasons. Our methods are applicable to any taxa with repeated occurrence data across large scales.

Intersection control based on Fine-grained Phases for connected and automated vehicles

Traditional traffic signal control is one of the most widely used urban traffic control methods, due to its high efficiency, robustness, and simplicity. However, in the era of connected and automated vehicles (CAVs), it is prospective to enhance traffic control precision and achieve vehicle coordination in a more efficient manner. Focusing on an isolated intersection, this study proposes an innovative concept of Fine-grained Phase (FP) for CAV-enabled traffic control, which realizes more refined right-of-way allocation than traditional signal phases while preserving the simplicity and scalability. The development of FP jointly optimizes traffic control strategy and intersection layout design to fully utilize time-space resources, and multiple forms of FP could be generated in response to various traffic demands; meanwhile, vehicle coordination is achieved in an offline–online manner to balance solution quality and efficiency. Under the FP-based control, each lane is assigned with periodical entry time points using FP design results, then vehicles are supposed to catch up with the appropriate time points to fulfill their trips. Theoretical analyses are conducted under stationary vehicle arrival patterns, where the average delay and the intersection capacity associated with different forms of FP can be analytically acquired. For the implementation of FP-based control, we construct a general offline–online framework, consisting of an offline model that optimizes the geometry of the conflict zone at intersections and the control strategy of vehicles simultaneously, which is formulated as a mixed-integer linear programming problem, and an online FP adoption mechanism that can adapt to varying demand patterns. Under the framework, limited online computation is required, facilitating its practical real-time implementation. Simulation studies under a variety of demand settings show that FP-based control is advantageous over benchmark methods in reducing vehicle delay and also improving intersection capacity in most cases; even for cases with extremely imbalanced demands, FP-based control can admit comparable throughput, while the average delay is much smaller. In addition, results of computation time reveal that FP-based control could realize real-time implementation with negligible computation burden.